npx skills add ...
npx skills add wshobson/agents --skill python-code-style
Python code style, linting, formatting, naming conventions, and documentation standards. Use when writing new code, reviewing style, configuring linters, writing docstrings, or establishing project standards.
npx skills add wshobson/agents --skill python-code-style
Consistent code style and clear documentation make codebases maintainable and collaborative. This skill covers modern Python tooling, naming conventions, and documentation standards.
Let tools handle formatting debates. Configure once, enforce automatically.
Follow PEP 8 conventions with meaningful, descriptive names.
Docstrings should be maintained alongside the code they describe.
Modern Python code should include type hints for all public APIs.
Use ruff as an all-in-one linter and formatter. It replaces flake8, isort, and black with a single fast tool.
Run with:
Configure strict type checking for production code.
Alternative: Use pyright for faster checking.
Follow PEP 8 with emphasis on clarity over brevity.
Files and Modules:
Classes and Functions:
Constants:
Group imports in a consistent order: standard library, third-party, local.
Use absolute imports exclusively:
Write docstrings for all public classes, methods, and functions.
Simple Function:
Complex Function:
Class Docstring:
Set line length to 120 characters for modern displays while maintaining readability.
README Structure:
CHANGELOG Format (Keep a Changelog):
# pyproject.toml
[tool.ruff]
line-length = 120
target-version = "py312" # Adjust based on your project's minimum Python version
[tool.ruff.lint]
select = [
"E", # pycodestyle errors
"W", # pycodestyle warnings
"F", # pyflakes
"I", # isort
"B", # flake8-bugbear
"C4", # flake8-comprehensions
"UP", # pyupgrade
"SIM", # flake8-simplify
]
ignore = ["E501"] # Line length handled by formatter
[tool.ruff.format]
quote-style = "double"
indent-style = "space"ruff check --fix . # Lint and auto-fix
ruff format . # Format code# pyproject.toml
[tool.mypy]
python_version = "3.12"
strict = true
warn_return_any = true
warn_unused_ignores = true
disallow_untyped_defs = true
disallow_incomplete_defs = true
[[tool.mypy.overrides]]
module = "tests.*"
disallow_untyped_defs = false[tool.pyright]
pythonVersion = "3.12"
typeCheckingMode = "strict"# Good: Descriptive snake_case
user_repository.py
order_processing.py
http_client.py
# Avoid: Abbreviations
usr_repo.py
ord_proc.py
http_cli.py# Classes: PascalCase
class UserRepository:
pass
class HTTPClientFactory: # Acronyms stay uppercase
pass
# Functions and variables: snake_case
def get_user_by_email(email: str) -> User | None:
retry_count = 3
max_connections = 100# Module-level constants: SCREAMING_SNAKE_CASE
MAX_RETRY_ATTEMPTS = 3
DEFAULT_TIMEOUT_SECONDS = 30
API_BASE_URL = "https://api.example.com"# Standard library
import os
from collections.abc import Callable
from typing import Any
# Third-party packages
import httpx
from pydantic import BaseModel
from sqlalchemy import Column
# Local imports
from myproject.models import User
from myproject.services import UserService# Preferred
from myproject.utils import retry_decorator
# Avoid relative imports
from ..utils import retry_decoratordef get_user(user_id: str) -> User:
"""Retrieve a user by their unique identifier."""
...def process_batch(
items: list[Item],
max_workers: int = 4,
on_progress: Callable[[int, int], None] | None = None,
) -> BatchResult:
"""Process items concurrently using a worker pool.
Processes each item in the batch using the configured number of
workers. Progress can be monitored via the optional callback.
Args:
items: The items to process. Must not be empty.
max_workers: Maximum concurrent workers. Defaults to 4.
on_progress: Optional callback receiving (completed, total) counts.
Returns:
BatchResult containing succeeded items and any failures with
their associated exceptions.
Raises:
ValueError: If items is empty.
ProcessingError: If the batch cannot be processed.
Example:
>>> result = process_batch(items, max_workers=8)
>>> print(f"Processed {len(result.succeeded)} items")
"""
...class UserService:
"""Service for managing user operations.
Provides methods for creating, retrieving, updating, and
deleting users with proper validation and error handling.
Attributes:
repository: The data access layer for user persistence.
logger: Logger instance for operation tracking.
Example:
>>> service = UserService(repository, logger)
>>> user = service.create_user(CreateUserInput(...))
"""
def __init__(self, repository: UserRepository, logger: Logger) -> None:
"""Initialize the user service.
Args:
repository: Data access layer for users.
logger: Logger for tracking operations.
"""
self.repository = repository
self.logger = logger# Good: Readable line breaks
def create_user(
email: str,
name: str,
role: UserRole = UserRole.MEMBER,
notify: bool = True,
) -> User:
...
# Good: Chain method calls clearly
result = (
db.query(User)
.filter(User.active == True)
.order_by(User.created_at.desc())
.limit(10)
.all()
)
# Good: Format long strings
error_message = (
f"Failed to process user {user_id}: "
f"received status {response.status_code} "
f"with body {response.text[:100]}"
)# Project Name
Brief description of what the project does.
## Installation
\`\`\`bash
pip install myproject
\`\`\`
## Quick Start
\`\`\`python
from myproject import Client
client = Client(api_key="...")
result = client.process(data)
\`\`\`
## Configuration
Document environment variables and configuration options.
## Development
\`\`\`bash
pip install -e ".[dev]"
pytest
\`\`\`# Changelog
## [Unreleased]
### Added
- New feature X
### Changed
- Modified behavior of Y
### Fixed
- Bug in Z